Center for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (Fiocruz), Salvador, Brazil; Centre of Mathematics of the University of Porto (CMUP), Department of Mathematics, University of Porto, Porto, Portugal.
Department of Computer Science, Federal Rural University of Pernambuco, Recife, Brazil.
Lancet Digit Health. 2024 Aug;6(8):e570-e579. doi: 10.1016/S2589-7500(24)00099-2.
Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes.
In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017-23). We constructed a graph-based representation of Brazil's mobility network. The Ford-Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25-April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6-March 1, 2021), for the purposes of model validation.
We found that flights alone transported 79·9 million (95% CI 58·3-101·4 million) passengers annually within Brazil during 2017-22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25-April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6-16, 2021.
By providing essential clues for effective pathogen surveillance, our results have the potential to inform public health policy and improve future pandemic response efforts. Our results unlock the potential of designing country-wide clinical sample collection networks with mobility data-informed approaches, an innovative practice that can improve current surveillance systems.
Rockefeller Foundation.
病原体的检测和预测对于防止疾病的广泛传播至关重要。人类流动是传染病在人际间传播的一个基本问题。通过利用移动数据驱动的方法，我们旨在确定巴西的一些市辖区，这些地区可以构成一个先进的哨点网络，以便能够早期发现循环病原体及其相关的传播途径。
在这项建模和验证研究中，我们综合了巴西城市间流动的全面数据集，涵盖了巴西地理统计局（2016 年数据）、国家运输联合会（2022 年）和国家民用航空局（2017-23 年）的航空、公路和水路运输数据。我们构建了一个基于图的巴西流动网络表示。利用 Ford-Fulkerson 算法，根据它们作为哨点的适宜性对 5570 个巴西城市进行了排名，从而能够预测最适合早期检测的地点，并追踪新出现病原体的最可能轨迹。我们还获取了巴西各城市在新冠病毒早期（2020 年 2 月 25 日至 4 月 30 日）以及在马瑙斯出现伽马（P.1）变异株（2021 年 1 月 6 日至 3 月 1 日）时的 SARS-CoV-2 基因数据，以验证模型。
我们发现，仅在 2017-22 年期间，飞行就每年在巴西境内运送了 7990 万（95%CI，5830 万至 10140 万）名乘客，高峰期出现在春季末和夏季；公路和河网每周的最大运力为 7830 万乘客。通过分析起源于源节点的 7746479 条最可能路径，我们发现 3857 个城市完全覆盖了巴西所有 5570 个城市的流动模式，其中 557（10.0%）个城市覆盖了我们研究中 6313380（81.5%）的流动模式。通过将流动模式战略性地纳入巴西现有的流感样疾病监测网络（即，将 199 个哨点中的 111 个位置转移到不同的市辖区），我们的模型预测，如果不增加哨点的数量，移动性覆盖率将从 4059155（52.4%）的流动模式提高到 5422535（70.0%）。我们的发现与在 SARS-CoV-2 大流行期间收集的基因组数据相吻合。我们的模型准确地映射了 22（51%）个受 clade 1 影响的城市和 28（60%）个受 clade 2 影响的城市（由圣保罗市传播），以及 20（49%）个受 clade 1 影响的城市和 28（58%）个受 clade 2 影响的城市（由里约热内卢市传播），这些都发生在 2020 年 2 月 25 日至 4 月 30 日期间。此外，在 307 个建议用于检测马瑙斯出现的病原体的早期检测地点中，有 224（73%）个与 2021 年 1 月 6 日至 16 日出现的 gamma 变异株传播的第一批受影响城市相对应。
通过为有效的病原体监测提供重要线索，我们的研究结果有可能为公共卫生政策提供信息，并改善未来的大流行应对工作。我们的研究结果揭示了利用移动数据制定具有创新性的全国临床样本收集网络的潜力，这一创新实践可以改进现有的监测系统。
洛克菲勒基金会。
